Detectability of Neutrinos from Failed Supernovae and Black Hole-Neutron Star Mergers

1. Detectability of Neutrinos from
Failed Supernovae and Black Hole-
Neutron Star Mergers
Halston Lim and Jason Liang
North Carolina School of Science and Mathematics
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2. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Neutrinos and their Detection
• Fundamental particles
– Three flavors (νe, νμ, ντ)
– Mainly interact through weak force
• Can propagate through matter
– Useful when astrophysical phenomena
are opaque to light
– Detectors use secondary particles to The fundamental particles
of the Standard Model
determine if event has occurred
• Detectors
– Water Cherenkov (Super-Kamiokande)
– Liquid argon (LBNE)
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

3. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Neutrino Emission
• Neutrinos emitted by
astrophysical phenomena
• Core collapse supernova (SN) –
stellar collapse and explosion
• Emits neutrinos (99% of binding
energy)
Supernova 1987A
• Analyzed important events different from typical
SN
- Failed supernovae (fSN)
- Black hole-neutron star mergers (BHNSM) 3
Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

4. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
fSN and BHNSM
• fSN
- Very high-mass star
- Site of nucleosynthesis
- Would allow for first
observation of BH
Artist’s conception of a fSN
formation
• BHNSM
- Thought to be linked with short-
period gamma-ray bursts
- Very luminous events
- Can be used to study the
evolution of early universe Artist’s conception of a BHNSM
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

5. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Research Goals
1. Determine observability of neutrinos from fSN
and BHNSM in current and proposed detectors
2. Compare detector signals from our events with
signals from typical SN
3. Investigate how well the
parameters of the original flux
distribution can be determined
from interaction rates Schematic of Super-Kamiokande
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

6. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
General Methods
Use theoretical After finding the
models of fSN neutrinos emitted, Determine the
and BHNSM to use SNOwGLoBES observability of
calculate the to calculate the neutrinos
neutrino what detectors on
emission Earth observe
Consider existing
astrophysical models of
fSN and BHNSM and
how observations will
confirm/reject these
Neutrino event generated models
with Superscan event
display program
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

7. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
SNOwGLoBES
(SuperNova Observatories with General Long Baseline Experiment Simulator)1
• Interaction rates calculator that we used to simulate neutrino events on
Earth
• We calculated the neutrino flux from fSN and BHNSM and integrated real
detectors parameters (cross sections, smearing, efficiencies)
[1] K. Scholberg, in APS April Meeting 2011 (2011), p. 1.
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

8. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Fluence Calculation
[2] H. Minakata et al., Journal of Cosmology and Astroparticle Physics 2008, 006 (2008).
[3] K. Sumiyoshi, S. Yamada, and H. Suzuki, The Astrophysical Journal 667, 32 (2007).
[4] O. L. Caballero and G. C. McLaughlin, Physical Review D 80, 123004 (2009).
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

9. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Examples of Flux Parameterizations
Garching Parameterization 3
Fermi-Dirac Parameterization 3
Best to model fSN Best to model BHNSM
(non-thermal emission) (thermal emission)
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

10. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Flux Comparisons with Typical SN
SH fSN BHNSM
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4
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• fSN and BHNSM have higher neutrino energies
[5] G. Shen, arXiv Preprint arXiv:1202.5791 1–20 (2012).
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

11. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
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Event Rate Calculation
Flux Interaction Threshold Response x
Cross Section x Energy Resolution
Secondary
Particle Distribution
• Calculated the flux
– Applied intrinsic interaction and detection inputs for various
detectors in SNOwGLoBES6
• Main focus on liquid argon (LBNE) and water Cherenkov
(Super-K) detectors
[6] K. Scholberg, arXiv Preprint arXiv:1205.6003 1–19 (2012).
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

12. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
a.
Neutrino Detector Signal b.
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LEFT: The total number of events as a function of threshold energy is plotted for typical SN, fSN, and
BHNSM models.
RIGHT: The total number of events is shown as a function of time for typical SN (Livermore, Basel) and fSN
models (SH and LS nuclear equation of states. All events are calculated in Super-Kamiokande at 10 kpc.
[7] J. M. Lattimer and F. D. Swesty, Nuclear Physics A 535, 331 (1991).
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

13. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
a.
Observability b.
SH fSN BHNSM
The total number of events in various current (Super-Kamiokande, HALO, LVD, Borexino) and proposed
(Hyper-Kamiokande, LENA, GLACIER, LBNE) detectors as a function of distance. Water Cherenkov
(blue), liquid scintillator (red), and liquid argon (green) detectors are shown.
Viewing neutrinos from our neighbor Andromeda (700 kpc) is feasible with new detectors. 13
Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

14. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Observed Neutrino Events
Center of Milky Way (10 kpc) Andromeda (700 kpc)
fSN (32 kt Super-K) 37400 events 8 events
fSN (560 kt Hyper-K) 654000 events 134 events
BHNSM (32 kt Super-K) 9300 events 2 events
BHNSM (560 kt Hyper-K) 162000 events 33 events
• Supernova 1987A, which exploded in the Large Magellanic
Cloud 50 kpc away, only produced 20 neutrinos that were
detected
• Only confirmed observation of astrophysical neutrinos to date
• The next supernova event would give many more neutrinos!
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

15. Introduction SNOwGLoBES Observed Signal Time EvolutionParameter Determination Conclusion
Neutronization Burst
fSN neutrino luminosities3
Neutronization burst
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

16. Introduction SNOwGLoBES Observed Signal Time EvolutionParameter Determination Conclusion
Neutronization Burst Visibility
Water Cherenkov (Super-K) Liquid Argon (LBNE)
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

17. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Nucleosynthesis
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

18. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

19. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Nucleosynthesis Potential
GR parameterization for fSN FD parameterization for BHNSM
Temperature
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

20. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Results
• Showed differences between typical SN
detector signal and fSN/BHNSM detector
signals
• Calculated the number of observed events
from fSN and BHNSM in current and
proposed detectors
• Determined the potential for nucleosynthesis
to occur in fSN and BHNSM 20
Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

21. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Future Work
• Incorporate systematic uncertainties in parameter
determinations
• Apply flavor dependent flux parameterizations to
improve fits
• Use time-dependent models of BHNSM
• Incorporate neutrino oscillation
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013

22. Introduction SNOwGLoBES Observed Signal Time Evolution Parameter Determination Conclusion
Credits
Dr. Kate Scholberg, Duke University
Dr. Josh Albert, Duke University
Dr. Alex Himmel, Duke University
Dr. Jonathan Bennett, NCSSM
Poison Bear
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Neutrino Detection Halston Lim and Jason Liang, North Carolina School of Science and Mathematics, Sigma Xi 2013